Many organizations and individuals use electronic data to improve their operations or aid their decision-making. For example, many business enterprises use data management technologies to enhance the efficiency of various business processes, such as executing transactions, tracking inputs and outputs, pricing or marketing products. As another example, many businesses use operational data to evaluate performance of business processes, to measure the effectiveness of efforts to improve processes, or to decide how to adjust processes.
The sheer volume of data available through transactional logs, social media, web traffic and other sources provides many opportunities to become a data driven organization. Moreover, the ability to model and learn from the data allows entities to adapt to changing environments and situations. However, to capitalize on this data is not that straightforward, and often requires highly-skilled data scientists to build and test models, and the process of using large, diverse and dynamic datasets to derive insights is tedious, costly and time consuming. Further, the process of consuming data from different sources and changing data requirements is an added overhead in the project which can affect development and implementation timelines. As such, many business entities are incapable of modeling the business problem, building and testing models that address the problem, and producing actionable results without highly specialized experts.
A systematic analytical platform needs to be established in order to define, develop, deploy and manage models with respect to problem to be solved.